# Automatic Text Summarization Methods: A Comprehensive Review

arXiv: https://arxiv.org/abs/2204.01849 · HTML: https://mlsanity.com/paper/2204.01849/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 102 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [Neural Extractive Text Summarization with Syntactic Compression](https://mlsanity.com/paper/1902.00863/) (2019)
- [Improving Abstraction in Text Summarization](https://mlsanity.com/paper/1808.07913/) (2018)
- [Language as a Latent Variable: Discrete Generative Models for Sentence Compression](https://mlsanity.com/paper/1609.07317/) (2016)
- [Deep Recurrent Generative Decoder for Abstractive Text Summarization](https://mlsanity.com/paper/1708.00625/) (2017)
- [Exploring Domain Shift in Extractive Text Summarization](https://mlsanity.com/paper/1908.11664/) (2019)
- [Text Summarization using Abstract Meaning Representation](https://mlsanity.com/paper/1706.01678/) (2017)
- [Query-oriented text summarization based on hypergraph transversals](https://mlsanity.com/paper/1902.00672/) (2019)
- [Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency](https://mlsanity.com/paper/1805.04033/) (2018)
- [Extractive Text Summarization using Neural Networks](https://mlsanity.com/paper/1802.10137/) (2018)

## Built on by (most cited first, 17 of 17)

- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering](https://mlsanity.com/paper/2304.13911/) (2023)
- [UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models](https://mlsanity.com/paper/2407.15359/) (2024)
- [PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT](https://mlsanity.com/paper/2305.11536/) (2023)
- [Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting](https://mlsanity.com/paper/2311.13274/) (2023)
- [A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence](https://mlsanity.com/paper/2402.12928/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2510.15436/) (2025)
- [Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review](https://mlsanity.com/paper/2303.14222/) (2023)
- [PublicHearingBR: A Brazilian Portuguese Dataset of Public Hearing Transcripts for Summarization of Long Documents](https://mlsanity.com/paper/2410.07495/) (2024)
- [Beyond the Frame: Single and mutilple video summarization method with user-defined length](https://mlsanity.com/paper/2401.10254/) (2023)
- [Comparison of Unsupervised Metrics for Evaluating Judicial Decision Extraction](https://mlsanity.com/paper/2510.01792/) (2025)
- [ArtContext: Contextualizing Artworks with Open-Access Art History Articles and Wikidata Knowledge through a LoRA-Tuned CLIP Model](https://mlsanity.com/paper/2602.11349/) (2026)
- [Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization](https://mlsanity.com/paper/2607.10825/) (2026)
- [Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa](https://mlsanity.com/paper/2608.19200/) (2026)

## Abstract (arXiv)

One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text is quite challenging for humans. So, it has increased the need for more complex and powerful summarizers. Researchers have been trying to improve approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-created summary. This study provides a detailed state-of-the-art analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research. The most commonly accepted approaches are extractive and abstractive, studied in detail in this work. Evaluating the summary and increasing the development of reusable resources and infrastructure aids in comparing and replicating findings, adding competition to improve the outcomes. Different evaluation methods of generated summaries are also discussed in this study. Finally, at the end of this study, several challenges and research opportunities related to text summarization research are mentioned that may be useful for potential researchers working in this area.